Many people feel overwhelmed by the need to know every tool and skill in data engineering. I compare this to learning a new language—it's not about trying to learn every word or phrase. Instead, the real goal is being FLUENT enough to communicate effectively. This mindset has guided my learning process over the past 10 years and is an approach I believe may help you as well. In this video, you'll learn: - The importance of fun projects for learning - The value of your own data playground stack - Leveraging a job to get paid to keep learning By the end, you'll understand how to approach learning data engineering with a focus on fluency, making your journey less stressful and, hopefully, more enjoyable. Enjoy!

Just because you can free access to open source code, does not mean it's 100% free. In this video, we'll review some of the big hidden costs associated with open source implementations so you're more aware and can make the best decision for your team.

Most data teams have similar players and types of work. This includes not just people on the core data engineering side, but others that operate around it. While the specific naming and responsibilities will vary by company, this video will break down some of the most common structures I've seen in my career. By the end, you'll walk away with a better understanding of common overall layouts to expect, or if you're trying to start your own team, how you can think about getting organized.

All things being equal, I think we'd all want access to source data in real time. This is the holy-grail of data engineering and removes most delays to insights. One architecture approach that makes this possible is known as the Kappa Architecture. It focuses on real-time data loading & processing rather than waiting on batches. No brainer, right? Well, back here in the real-world, things aren't made equal. Different architecture approaches require different levels of complexity (aka skill requirements). Which impacts design & maintenance time. Which all tends to mean higher overall cost of ownership. All that to say, just because it's technically possible, doesn't always mean it's the best choice for your team. Truthfully, I find it's usually not necessary. BUT that doesn't mean you shouldn't be aware of it and be able to evaluate it. So in this video we'll review at a high level what the Kappa architecture is about. By the end, you'll understand the key points and be able to decide whether or not it makes sense for your team. Enjoy!

In the modern data landscape, it's the tools & technologies that grab all of the headlines. But a potentially more important decision you need to make is about your data strategy. While there are many different approaches, today I want to cover one in particular known as "Modern Data Warehouse". This is the most common approach I've seen and is ideal for most small-mid size companies looking to establish their architecture. It's also important to remember that most companies aren't "big data" enterprises or require overly complex systems. Avoid the urge to keep up with big tech companies if you don't feel it applies to you. Which is probably the case. I'd take simplicity & clarity over complexity any day.